This is about Osmo, an AI company that gives computers a sense of smell and uses it to make perfumes. The founder thinks smell is the last sense to digitize, and AI is just starting to crack it, threatening the old fragrance industry. Key holdings: ①Generation (Osmo's own brand, cutting custom perfume delivery from 12-18 months to weeks); ②StockX (uses Osmo to detect fake sneakers in 20 seconds); ③traditional fragrance firms (stuck with 300-year-old model, 90% of clients get stock samples, but high margins and sticky customers).
Alex Wiltschko founded Osmo, a company dedicated to giving computers a sense of smell by teaching machines to "read" and "write" scents using AI technology. Its first commercial application, Generation, has disrupted the traditional perfume industry, dramatically accelerating the custom fragrance cr
Guest: Alex Wiltschko, Founder and CEO of Osmo, former Google Brain researcher, with a neuroscience background (Harvard) and AI startup experience (two companies acquired, previously joined Twitter's deep learning team). Main thread: How Osmo digitizes smell through AI (reading + writing scents), and uses this platform to enter the fragrance industry (Generation brand) and broader commercial applications. Most impactful take: Alex Wiltschko believes that digitizing smell is the "last piece of the puzzle" — computers can already see, hear, and touch, but smell is the only modality not yet conquered, and Osmo is climbing the AI S-curve for this modality, currently at the "very leftmost, just taking off" stage.
Alex Wiltschko emphasized that to achieve computer olfaction, both "read" and "write" capabilities are necessary, and "write" is the key to verifying whether "read" is correct.
Alex Wiltschko believes that the fragrance industry's 300-year-old business model (12-18 months customization cycle, 90% of clients receiving "stock samples" rather than genuine customization) is exactly where Osmo enters, and the Generation brand is its "first vertical application."
| Dimension | Traditional Fragrance Companies | Generation (Osmo) |
|---|---|---|
| Customization Process | Submit written brief → wait 3 months → receive stock samples → return → wait another 3 months → repeat for 12-18 months | Chat-like interface (similar to ChatGPT) → AI instant analysis → rapid formulation output |
| Customization Rate | Over 90% of clients receive non-genuine customized products | Genuine customization every time |
| Delivery Cycle | 12-18 months | Reduced to weeks/months |
| Value Proposition | Large clients, large scale, high barriers | Opens access to creators who previously could not access custom scents (e.g., Instagram creators) |
Alex describes Generation's workflow: Users describe their brand concept and desired scent direction (e.g., "the fresh air at the top of a sequoia forest"), and Osmo embeds the text into its 300-dimensional scent map ("not three-dimensional like RGB, but about 300 dimensions — which is why scent had to wait for AI to be digitized"). The system decodes these coordinates into a scent source file and displays where the scent falls on a map of the "top 100 mass-market best-selling fragrances." "What used to take months, we compressed into minutes."
This choice is backed by a deep market insight: Successful platforms typically enter markets where "there are already many active participants," but "there aren't many fragrance companies in the first place." Alex shared a similar case of Metropolis (a parking software company) — the parking industry is not a mature buyer of software, so they became a parking company. "If I could sell software, believe me, we tried... but we probably wouldn't be having this conversation." Therefore, Osmo chose to become a brand (Generation), serving end users directly, while retaining the platform capability.
Alex Wiltschko argues that the fragrance industry inherently possesses the characteristics of a "good business," but traditional players face the risk of disruption in the age of AI.
Here, Alex makes a structural judgment: AI sits at different points on the S-curve across modalities. Text is "basically done" ("We passed the Turing test, the training data is just one internet, and we've used it all up"), images are similar, and video still needs time. But for smell: "We are pushing AI up the S-curve for smell, and right now we are at the far left – just starting to take off." This means that traditional fragrance companies, if they rely on old models, will be left behind by the acceleration driven by AI.
Alex Wiltschko shared a key strategic philosophy: Osmo does not directly assault the cliff for the ultimate goal of "giving computers a sense of smell," but instead finds a route where "it can pause along the way to build a business."
Alex candidly lists the biggest uncertainties facing Osmo:
| Target | Guest Attitude | Key Data |
|---|---|---|
| StockX | Bullish (already partnered) | Detects fake shoes via smell (true/false determination), results in 20 seconds; sensor is currently about the size of two shoeboxes |
| Generation (Osmo's own brand) | Bullish (just launched) | Reduces custom fragrance cycle from 12-18 months to weeks/months; targets creators who previously couldn't access scent (e.g., Instagram creators) |
| Traditional fragrance industry (unnamed companies) | Risk warning (challenger) | Business model unchanged for 300 years; 90% of clients receive "stock samples"; repurchase rate >50%; recession-resistant, high margins |
| Ritz-Carlton, Gramercy Park Hotel | Neutral (example) | Already use custom scents as brand identity |
1. "Computer olfaction is the last piece of the puzzle" (Alex Wiltschko) — Computers can already see, hear, and touch, but smell has not yet been digitized. Osmo believes this is the last "human sensory modality" not yet penetrated by AI, and it is just beginning its ascent from the far left of its S-curve.
2. "300-dimensional map — smell must wait for AI to digitize" (Alex Wiltschko) — Smell is not a three-dimensional space like RGB, but approximately 300 dimensions, so traditional methods cannot handle it. Osmo uses graph neural networks (GNN) to map chemical structures into this high-dimensional space, achieving "predicting smell from molecular structure".
3. "AI predictions are more accurate than the average human evaluator" (Alex Wiltschko) — In double-blind tests, Osmo's AI predicted smells with higher accuracy than the average of a human panel. "If you need to add one more person to the panel, you'd rather ask the software."
4. "Don't climb the cliff directly; find a path with a gentle enough slope" (Alex Wiltschko) — To achieve the ultimate goal of "fully digitizing human olfaction", Osmo chose to first launch the Generation brand, build a sustainable business in the market, and accumulate data along the way. Each step makes the company "harder to kill" rather than riskier.
5. "The fragrance industry is essentially a manufacturing business, but its profit margins are at the level of non-manufacturing businesses" (Alex Wiltschko) — The reason is that "a large amount of proprietary know-how is embedded in the final product". Traditional players are safe, but AI may change this barrier.
6. "AI is at different positions on the S-curve for different modalities" (Alex Wiltschko) — Text is nearly complete ("there is only one internet, and it has been used up"), images are similar, video still needs time, and smell is just beginning. This means Osmo's space is much larger than the traditional industry appears.
7. "If you do well in one smell domain, it will automatically help you enter other adjacent domains" (Alex Wiltschko) — The molecular overlap between fruit/floral scent design and human health detection is high, so the Generation platform naturally lays the foundation for future health applications.
8. "Mother Nature may pull the rug out from under you" (Alex Wiltschko) — The biggest uncertainty is the science itself (whether 2025/2026/2027 will be the "year to crack the next layer of olfactory secrets"), as well as the physical miniaturization engineering challenges of sensors and printers.